Image segmentation method and device, equipment and storage medium

Abstract

The embodiment of the invention provides an image segmentation method and device, equipment and a storage medium, and relates to the technical field of artificial intelligence, and the method comprises the steps: extracting target image features of a target domain to-be-processed image through a trained target domain image segmentation network, obtaining a target domain prediction image based on the target image features, wherein the target domain prediction image comprises a target object with a target label, and the trained target domain image segmentation network is obtained by performing joint iterative training on a to-be-trained target domain image segmentation network and a to-be-trained source domain image segmentation network based on a domain transfer learning mode, and the source domain sample image comprises a source domain sample object with a source label. Through the domain transfer learning mode, the target domain image segmentation network used for segmenting the target domain image is obtained under the condition that each pixel in the target domain sample image does not need to be subjected to refined labeling, so that the manpower and the cost are reduced, and the image segmentation efficiency and the image segmentation accuracy are improved.

Description

technical field [0001] The embodiments of the present invention relate to the technical field of artificial intelligence, and in particular to an image segmentation method, device, equipment and storage medium. Background technique [0002] Image segmentation is a fundamental problem in video and image understanding. The image segmentation process is as follows: after the image x is input into the image segmentation model, the image segmentation model predicts the category of each pixel in the image x, and then according to the category of each pixel, the image x is segmented to obtain the predicted image y. [0003] Since the image segmentation model achieves image segmentation by predicting the category of each pixel in the image, it is also necessary to manually fine-tune each pixel in the sample image when training the image segmentation model. However, complex and diverse real scenes, such as weather and lighting, will cause differences in images of the same object. C...

AI Technical Summary

Problems solved by technology

However, the real scene is complex and diverse, such as weather, lighting, etc. will cause differences in the images of the same object

Collecting images in all scenes and giving corresponding fine annotations requires too much manpower and cost, and the efficiency is low

Images